89 citations · 240 across the 11 of their papers we have counts for
11 papers
PolyAlign: Conditional Human-Distribution Alignment
L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2
Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assistant behavior. While effective fo…
Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models
Haoyu Gao, Ting-En Lin, Hangyu Li +4
Task-oriented dialogue (TOD) systems facilitate users in executing various activities via multi-turn dialogues, but Large Language Models (LLMs) often struggle to comprehend these…
UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt
Yucheng Cai, Wentao Ma, Yuchuan Wu +4
Recent research has shown that multi-task pre-training greatly improves the model's robustness and transfer ability, which is crucial for building a high-quality dialog system. How…
CharBERT: Character-aware Pre-trained Language Model
Wentao Ma, Yiming Cui, Chenglei Si +3
Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almos…
Conversational Word Embedding for Retrieval-Based Dialog System
Wentao Ma, Yiming Cui, Ting Liu +3
Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR…
Benchmarking Robustness of Machine Reading Comprehension Models
Chenglei Si, Ziqing Yang, Yiming Cui +3
Machine Reading Comprehension (MRC) is an important testbed for evaluating models' natural language understanding (NLU) ability. There has been rapid progress in this area, with ne…
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots
Wentao Ma, Yiming Cui, Nan Shao +5
We consider the importance of different utterances in the context for selecting the response usually depends on the current query. In this paper, we propose the model TripleNet to…
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions
Zhipeng Chen, Yiming Cui, Wentao Ma +2
Machine Reading Comprehension (MRC) with multiple-choice questions requires the machine to read given passage and select the correct answer among several candidates. In this paper,…
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Yiming Cui, Ting Liu, Wanxiang Che +5
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in…
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension
Zhipeng Chen, Yiming Cui, Wentao Ma +3
This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In this paper, we present a neura…
Dataset for the First Evaluation on Chinese Machine Reading Comprehension
Yiming Cui, Ting Liu, Zhipeng Chen +3
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, existing reading comprehension datasets are mostly in Engl…